The present invention relates generally to medical imaging, and more particularly to iterative reconstruction of images.
In recent years, non-contrast magnetic resonance angiography (NCE MRA) has been an emerging tool in the diagnosis of degenerative vascular disease. Desired improvements include a reduction of the total scan time to reduce the impact of unintentional motion as well as an increase of the spatial and/or temporal resolution. Thus, the compressed sensing (CS) technique, which aims at reconstructing high quality signal from largely undersampled acquisitions, is a natural fit to further enhance time-resolved NCE-MRA applications and to generate 4D angiograms.
Magnetic Resonance Imaging (MRI) has become a well-established medical diagnostic tool for imaging structures within the body of a patient. Image quality may be characterized by a host of parameters, including resolution, field of view, contrast, edge definition, and artifacts (for example, ghosts and streaks). Under a broad range of conditions, image quality improves with increasing data acquisition time. If the data acquisition time is increased, however, the patient is subjected to a longer scan time, which increases patient discomfort. In some instances, long scan times may actually degrade image quality because of movement of the region of interest during the scan. Short scan times are also necessary for near-real-time measurements, such as used in functional MRI. There is, thus, a trade-off between image quality and scan time.
Images are displayed on physical media; for example, emulsions on film or pixels on a monitor. The normal physical world may be referred to as real space. In one method for producing high-quality images, MR signals are captured in k-space. In some fields of study, k-space is also referred to as spatial-frequency domain. In general terms, data values in real space are then generated by taking the inverse Fourier transform of data values in k-space. In general, MR signals are not measured as a continuous function of position in k-space. They are sampled at discrete k-values. Subject to specific constraints and boundary conditions, image quality generally improves as the density and range of discrete k-space sampling points are increased. Recording a large number of samples, however, has disadvantages. One is the extended scan time discussed above. The other is low temporal resolution.
To reduce data acquisition time, MRI data are often intentionally under-sampled. This will, however, often lead to reduced signal-to-noise ratio (SNR) and to image degradation. Various techniques have been developed to enhance the image quality reconstructed from under-sampled data, but they require extended computational time and high memory usage. Accordingly, novel and improved compressed sensing methods and systems are required which further enhance time-resolved NCE-MRA applications such as generating 4D angiograms.